GHENT UNIVERSITY

Faculty of Medicine and Health Sciences

Academic year 2011-2012

THE INFLUENCE OF PSYCHOSOCIAL CONSTRUCTS ON THE RELATIONSHIP

BETWEEN SOCIAL CAPITAL AND MENTAL HEALTH

Part one: scientific article

Master thesis presented to obtain the degree of

Master of Health Education and Health Promotion

By Alison Taylor

Promotor: Prof. Dr. Sara Willems

Co-promotor: Drs. Veerle Vyncke

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Putting the pieces together: Social support and resilience as intermediate variables

in the relationship between social capital and mental health.

Authors:

Vyncke, Veerlea (MA)

Taylor, Alisonb (MA)

Hardyns, Wimc (PhD.)

Pauwels, Lievend (Prof. Dr.)

Willems, Sarae

(Prof. Dr.)

abce University of Ghent, faculty Medicine and Health Science, department of General Medical Practice

and Primary Care, 1K3, De Pintelaan 185, 9000 Ghent, Belgium. (Veerle.Vyncke@UGent.be;

Wim.Hardyns@UGent.be; Alison.Taylor@UGent.be; Sara.Willems@UGent.be)

acResearch Foundation Flanders, Egmontstraat 5, 1000 Brussel.

d University of Ghent, faculty Study of Law, department of Criminal Law and Criminology,

Universiteitstraat 4, 9000 Ghent, Belgium. (Lieven.Pauwels@UGent.be)

Corresponding author: Vyncke Veerle, University of Ghent, faculty Medicine and

Health Science, department of General Medical Practice and Primary Care, 1K3, De

Pintelaan 185, 9000 Ghent, Belgium.Veerle.Vyncke@UGent.be; (tel) +3293326082;

(fax) +3293326082

Acknowledgements: The authors wish to thank the Research Foundation Flanders for the

financial support to the SWING-Survey. Furthermore, we would like to thank the City

of Ghent for the contribution to the sample selection, the students of University Ghent

for obtaining the interviews and the participants of Ghent.

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Abstract

Social capital is one of the determinants that is believed to contribute to a better mental

health (Conrad, 2010; De Silva et al., 2005; De Silva, 2006). Although the relationship

between social capital and mental health has extensively been explored, the underlying

mechanisms largely remain unknown (Rostila, 2011; Thoits, 2011). This study

examines the role of social support and resilience as intermediate variables in the

relationship between social capital and mental health. Data from the SWING survey

2011, which collected data in 50 neighbourhoods in Ghent (Belgium) are used

(N=1024).

The Baron & Kenny method (1986) and the Sobel test (1982) are used to answer the

research question. The analyses showed that both social support and resilience mediate

the relationship between having many social ties in the low social class (high level of

number of accessed positions in low social class) and a better mental health. The Sobel

test (1982) shows that the association between social capital and mental health via

social support is significant for all social capital variables (number of accessed

positions, number of accessed positions in high social class, number of accessed

positions in low social class and occupational status of alters in people’s network).

In sum, this study mainly identifies social support as intermediating variable in the

relationship between indicators of social capital and mental health, whereas the results

concerning resilience are less unambiguous. Further research should explore the role of

additional determinants of mental health as intermediate variables in this association

(Fisher & Baum, 2010). Moreover, longitudinal studies are recommended to examine

the causal pathways between social capital and mental health (Thoits, 2011).

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Further research is needed to inform policy makers and developers of health

interventions on the importance of psychosocial constructs for mental health and the

pathways linking social capital to mental health.

References:

Almedom A.M. (2005). Social capital and mental health. An interdisciplinary review of

primary evidence. Social Science & Medicine, 61, 943-964.

Baron, R.M., & Kenny, D.A. (1986). The moderator-mediator variable distinction in

social psychological research: Conceptual, strategic and statistical considerations.

Journal of Personality & Social Psychology, 51, 1173-1182.

Conrad, D. (2010). Social capital and men’s mental health. In D. Conrad & A. White

(Eds.), Promoting men’s mental health, pp. 26-38.

De Silva, M.J. (2006). Systematic review of the methods used in studies of social

capital and mental health. In: McKenzie K., Harpham, T. (Eds). Social capital and

mental health (pp. 39-67).London: Jessica Kingsley.

De Silva, M.J., McKenzie, K., Harpham, T., & Huttly, S.R.A. (2005). Social capital and

mental illness: a systematic review. Journal of epidemiology and community health,

59(8), 619-627.

Fisher, M., & Baum, F. (2010). The social determinants of mental health: implications

for research and health promotion. Australian and New Zealand Journal of

Psychiatry, 44, 1057-1063.

Rostila, M. (2011). A resource-based theory of social capital for health research: can it

help us bridge the individual and collective facets of the concept? Social Theory &

Health, 9(2), 109-129.

Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural

equation model. In S. Leinhardt (Ed.), Sociological Methodology, pp. 290-312.

Washington DC: American Sociological Association.

Thoits, P.A. (2011). Mechanisms linking social ties and support to physical and mental

health. Journal of Health and Social Behavior, 52(2), 145-161.

Keywords: social capital, social networks, mental health, psychosocial constructs,

social support, resilience, position generator, social resource

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Introduction

Mental health problems are one of the top three causes of life-years lost to disability

worldwide (McKenzie & Harpham, 2006). According to the Belgian Health Interview

Survey of 2008, a quarter of the Belgian population experiences mental health

complaints (Gisle, 2008). In1946, the World Health Organisation has defined health as

“a state of complete physical, mental and social well-being and not merely the absence

of disease or infirmity” (WHO, 1946). This definition reflects the evolution towards a

holistic approach of health, which acknowledges mental health as a valuable aspect of

population health (Morrens, 2008) and underlines the role of psychosocial constructs as

determinants of health.

Psychosocial constructs are intrinsic and extrinsic elements that can enhance well-being

either directly or indirectly by reducing the negative effects of stressful incidents in

daily and crisis situations (Ensel & Lin, 1991).

Social capital is one of these psychosocial constructs. In the past decennia, researchers

have used the term ‘social capital’ to refer to a myriad of aspects of the social

environment. Although a clear definition of the concept is still lacking, most definitions

of social capital include one of the following three elements: norms of reciprocity, trust

and social networks (Ferlander, 2007). Social capital has repeatedly been associated

with different aspects of mental health (De Silva et al., 2005; De Silva, 2006; Yip et al.,

2007; Hamano et al., 2010; House & Kahn, 1985, in Harpham et al., 2002; Hawe &

Shiell, 2000 in Giordano & Lindström, 2011; Heaney & Israel, 2008; Kawachi &

Berkman, 2001) and is thought to protect against common mental disorders, such as

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anxiety and depression (De Silva et al., 2007; Fujiwara & Kawachi, 2008; Stafford et

al., 2008; Webber & Huxley, 2007).

Although some researchers have tried to explain the relationship between social capital

and (mental) health (e.g. Cohen & Wills, 1985; Berkman et al., 2000), only a few

explore the theoretical pathways of the mechanisms involved (Rostila, 2011; Thoits,

2011).

This study aims to contribute to this missing evidence, by exploring the role of two

other psycho-social constructs, being social support and resilience, in the relationship

between social capital and mental health. The lack of clarity in the operationalisation of

social capital hinders researchers in drawing clear conclusions about the concept and the

way in which it operates (Derose & Varda, 2009). A social resource based

operationalisation of the concept has been suggested as a manner in which researchers

can focus on the core of social capital, namely resources in social networks (Lin, 2000;

Rostila, 2011; Van der Gaag, 2005). Within this operationalisation, social capital is

defined as “the resources embedded in one’s social networks, resources that can be

accessed or mobilized through ties in networks” (Lin, 2001, p.73)”. A social resource

based approach to social capital could be of particular interest for this study as it is

believed to enable the analysis of the mechanisms between social capital and health

(Rostila, 2011)

Social support

One of the mechanisms that might link social capital to mental health is social support

(Conrad, 2010; Song & Lin, 2010). Social support refers to the perceived and/or

received aid from network members (Song, 2010).

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Although social capital and social support are both relationship-based determinants of

health and very much related (Song, 2010; Song & Lin, 2009), they are not identical

(Almedom, 2005). Research has shown that social capital is associated to health,

beyond the effect of social support (Song & Lin, 2009; Haines et al, 2011; Verhaeghe et

al, 2012). Following a resource based operationalisation of social capital, social capital

captures the resources possessed by alters in one’s network, available to the individual

through investment in his social network (Song, 2010). Thus, social capital can clearly

be distinguished from social support, which refers to different forms of aid individuals

receive or perceive from their network members (Berkman 1984; House 1981).

Social support is a fundamental determinant of self-reported health (Poortinga, 2006b),

physical health and mental health above all (e.g. Gazzangiga & Heatherton, 2003;

Thoits, 2011; Withley & McKenzie, 2005). The association between social support and

mental health is well-established. Social support provides emotional stability, which

contributes to mental health (Caplan, 1974 in Withley & McKenzie, 2005). Moreover,

literature shows that social support can buffer the negative effects of general life stress

on depression (Kim et al., 2005; Thoits, 2011). Some studies have also explored the

indirect relationship between aspects of social networks or social capital and mental

health via social support; social support has been included in theoretical models that

link aspects of social networks to health (Berkman et al., 2000) (Cohen & Wills, 1985).

Furthermore, a recent Belgian study showed that social support partially explains the

association between social capital and self-rated health (Verhaeghe et al., 2012).

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Resilience

Social support is not the only pathway through which social capital might influence

mental health (Berkman et al. 2000; Conrad 2010). Resilience is another psychosocial

construct which might play a role in this relationship.

Antonovksy originally introduced the concept of resilience with the term ‘sense of

coherence’ (SOC) (1979, in Antonovsky & Sagy, 1986). He defined SOC as the degree

to which someone experiences his life as ordered, predictable and manageable.

Someone with a strong SOC is less inclined to experience stressful events as threatening

and frightening. Resilience is currently described as a set of attitudes and behaviours

associated with adaptive coping strategies (Windle et al., 2011). People with a high

level of resilience are characterized by a higher level of mastery, pro-social behaviour,

more self-esteem and a higher level of optimism (Rutter, 1985; Cederblad, 1996;

Lamand et al., 2008).

First of all, a high level of resilience has been related to better mental health (Connor et

al., 1999; Troy & Mauss, 2011), higher subjective well-being (Burns et al., 2011) and

more positive adaptive behaviours towards negative life experiences (e.g. coping with

stress) (Charney 2004; Aspinwall & MacNamara, 2005; Bonanno 2004, in Norris et al.,

2008; Butler et al., 2007, in Norris et al., 2008). A low degree of resilience is also

believed to increase the vulnerability to anxiety and depression (Burns et al., 2011).

Secondly, resilience has been linked to aspects of social networks, although the

evidence on this relationship is limited. Wolf et al. (2010) found that elderly with close

social networks possess higher levels of resilience. Resilience has also been associated

to social support: the support by family, friends and acquaintances helps individuals to

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use effective coping strategies during stressful moments (Buikstra et al., 2010).

Unfortunately, to our knowledge, no studies are available that explicitly link social

capital to resilience.

This study will be among the first to explore the relationship between social capital and

resilience.

We hypothesize that social capital is positively associated to resilience (hypothesis 1).

As literature shows that resilience is negatively related to stress (Amirkhan & Greaves,

2003; Dumont & Provost, 1999; Rutter, 1985; Tugade & Fredrickson, 2004; Lee et al.,

2011), we further suggest that higher levels of resilience lead to better mental health

(hypothesis 2).

Furthermore, based on the literature review, we hypothesize that the relationship

between social capital and mental health runs through the intermediate variables social

support (hypothesis 3) and resilience (hypothesis 4).

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Data and methods

This study is based on the Social ecology of Well-being In Neighbourhoods in Ghent

(SWING) Survey 2011. The SWING Survey 2011 was designed to provide information

on the role of social processes, at both the neighbourhood and the individual level, in

social disparities in health at both levels of analysis. The SWING Survey 2011 is part of

a four year research project financed by the Research Foundation - Flanders (FWO).

This study was approved by the Ethical Commission of Ghent University (reference

numbers 2011/458 and 2011/676).

Sampling

The SWING Survey 2011 reached a representative sample of 1.025 inhabitants in

Ghent. Ghent is the fifth-largest city of Belgium and located in Flanders, the northern

part of Belgium . The city covers 158 km² with a population of approximately 247.000

residents and is divided into 201 ‘statistical sectors’. A statistical sector, which is

comparable to the census tract level in the Anglo-Saxon system, is the smallest

administrative level on which objective administrative data (demographic, social and

economic indicators) are available. In the current study, statistical sectors are used to

operationalize neighborhoods.

Fifty of the 201 statistical sectors in Ghent are purposely selected, striving for a

representative sample of neighborhoods with regard to population density and

deprivation level. In each statistical sector, a representative sample of inhabitants is

selected from the National Population Register. This sample is stratified based on sex,

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age and nationality (Belgian or not Belgian). To be included in the study, respondents had to

(1) be older than 18 years, (2) have sufficient knowledge of the Dutch language to complete the

questionnaire and (3) give informed consent. People living in a residential setting (e.g. home

for the elderly, prison, etc.) could not participate in the study.

The questionnaire was partly administered face-to-face. Questions that were too

sensitive and could possibly lead to higher non-respons when administred face-to-face

(e.g. questions on income and financial difficulties, alcohol and drug use) were gathered

using a short self-administered questionnaire, that was handed over to the respondents

after completion of the face-to-face partim.

Dependent variable

The dependent variable in this study is mental health. The 5-item index on symptoms of

depression and nervousness from the MOS 36-Item Short-Form Health Survey (SF-36)

(van der Zee & Sanderman, 2011) is used to measure mental health. The respondents

are asked to rate how they felt during the past four weeks, using a 6 - point Likert scale

to answer the following items: (1) ‘Have you been a very nervous person’, (2) ‘Have

you felt so down in the dumps that nothing could cheer you up?’, (3) ‘Have you felt

calm and peaceful?’, (4) ‘Have you felt downhearted and blue?’, (5) ‘Have you been a

happy person?’. The mental health scale has a good reliability ( =0.79), which is

supported by previous research The dependent variable mental health is included in the

analyses as a dummy variable. A low group (code 0), referring to a worse mental health,

and a high group (code 1), referring to a better mental health, are distinguished by using

the median of the original variable.

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Independent variables

The Position Generator measures social capital defined as the resources embedded in

social networks (Lin & Erickson, 2008). The Position Generator in the current study is

based on the 20-item Position Generator used in the Social Cohesion Indicators in

Flanders (SCIF) study (Hooghe et al., 2009), with some minor adjustments based on

performed cognitive interviews. The instrument lists a number of occupations and asks

whether the respondent knows someone having these specific professions. The

occupations are purposely chosen to represent divers economic disciplines and cover the

whole socio-economic spectrum (M. Van der Gaag, 2005).

The Position Generator contains information on both the volume and the composition of

network resources (Van der Gaag, 2005; Lin & Erickson, 2008).

First, the volume of network resources is measured by calculating the total number of

accessed positions (M. Van der Gaag, 2005; M. P. J. Van der Gaag & Snijders, 2005).

For each occupational position, a dummy variable was made in this study that reflected

whether or not the respondent knows someone with this occupation. The number of

accessed positions, calculated by summing up the dummy variables that reflect the

availability of different occupational positions in the respondent’s personal network,

refers to people’s network size and shows to have a good reliability across two parallel

occupational lists (P. P. Verhaeghe et al., 2013).

Secondly, the position generator gives information on the composition of network

resources. The International Socio-Economic Index of occupational status (ISEI)

(Ganzenboom et al., 1992) is used to determine the occupational prestige linked to each

occupational group. The ISEI refers to the general income and educational level of

people in a certain occupational position: each occupational group is given an ISEI

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score based on the result of a weighted sum of the average education and the average

income of occupations in a sample of over 70 000 males aged between 21-64 in 16

countries (Ganzenboom et al., 1992; P. P. Verhaeghe et al., 2013). The ISEI-score is

determined for each of the twenty occupations of the position generator. For the

categories ‘someone who is on welfare’ and ‘someone who is unemployed’, the

occupational prestige scores determined by the researchers of the SCIF-survey were

copied (0 and 5 respectively). The mean occupational position represents the general

level of resources that are available for the respondent through his/her social contacts

and is calculated by taking the mathematical mean of the ISEI-scores of all the positions

the respondent has access to via his social network. The ISEI classification can also be

used to distinguish between alters from different socio-economic classes. This enables

two class-based measures of social capital: the number of accessed positions in high

social class and the number of accessed positions in low social class. A categorisation

that distinguishes between two broad social classes is found to have a good reliability

across two versions of the Position Generator in a parallel-test experiment (P. P.

Verhaeghe et al., 2013).

The indicators from the Position Generator are included in the analyses as categorical

variables. First, the quartiles were determined for each indicator, leading to a variable

that distinguishes between a low (first quartile / reference category), middle (second and

third quartile) and high level (fourth quartile) of social capital.

Resilience and social support are used as intermediate variables to explore the indirect

effect between the independent and dependent variables.

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Resilience is assessed using two items ( = .74) from the Brief Resilience Scale (Smith

et al., 2008). Respondents are asked to evaluate their ability to bounce back or recover

from stress on a five point Likert scale ( ‘totally not agreed’ to ‘totally agreed’ ). This

variable is included in the analyses as a dichotomous variable that distinguishes a low

(lower than the median of the variable; code 0) and a high level (higher than the median

of the variable; code 1) of resilience.

Social support and social influence measures the respondent’s perception of received

social support and ruling social norms in their network and is based on items from the

MOS Social Support Scale (Sherbourne & Stewart, 1991) and the Resource Generator

by Lannoo & Devos (unpublished work). The scale consists of 11 items (α=0.93), and

asks how many of the respondents’ friends, family members or acquaintances ‘(1)

understand your problems?’ ‘(2) would take you to the doctor/hospital when you are too

sick to go there yourself?’ ‘(3) would let you move into their house for a week if you

temporarily could not stay at your house?’ ‘(4) would help you with a little job you

couldn't do without help, e.g. moving heavy furniture in the house?’ ‘(5) would help

with daily chores if you were sick’ ‘(6) would be able to give advice on the invoice if

you would wonder why you had to pay so much at the doctor/dentist’ ‘(7) would be able

to give you legal advice (e.g. when you have conflicts with your landlord, your boss,

local authorities, etc.)’ ‘(8) would be able to give advice in case of a conflict within your

family’ ‘(9) would encourage you to exercise (e.g. walking, dancing, riding your bike,

doing sports’ ‘(10) would encourage you to eat healthy’ ‘(11) would encourage you to

go to the doctor if you experience health problems?’. The number of forms of support

the respondent believes he/she could access through his/her network is reflected in the a

general sum score. This variable is included in the analyses as a dichotomous variable

18

that distinguishes a low (lower than the median of the variable, code 0) and a high level

(higher than the median of the variable, code 1) of social support.

All analyses are controlled for three socio-demographic variables: sex, age and income

per capita. Gender is coded as zero for men and one for women. Respondents’ age in

years is calculated based on their birth year. Respondents were asked to estimate their

total net income (including wages, salaries, benefits, child support etc.) using 13

intervals of 500 euro, ranging from 0-499 € to 10.000 € or more. The income per capita,

a measure for income that is weighted based on the household size, was calculated to be

able to compare the income between the respondents. The OECD modified equivalent

scale was used to calculate this measure (Atkinson et al., 2001).

Statistics

The Baron & Kenny method (1986) was used to examine the intermediate effect of

social support and resilience (intermediate variables I) on the relationship between

social capital (independent variable X) and mental health (dependent variable Y). Using

the software program SPSS Statistics 19.0, three logistic regression models are built for

every indicator of social capital. The choice for logistic regression models can be

attributed to distributional problems in the intermediate and dependent variables, and

the non-linear relationship between social capital and mental health. The logistic

regression models follow the theory of Baron & Kenny (Baron & Kenny, 1986) (Figure

1), which tests mediation.

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(Please insert Figure 1 here)

Model 1 describes the association between social capital and mental health (path c).

Model 2 examines the association between the intermediate variables (social support

and resilience) and mental health. Model 3 describes the association between the

intermediate variables and mental health while controlling for social capital on the one

hand, and between social capital and mental health while controlling for the

intermediate variables on the other hand (path c’). The Baron & Kenny method tests

indirectly estimates the relationship between social capital and mental health via the

intermediate variables based on estimates of different paths in three statistical models

(Hayes, 2009). Baron & Kenny state that a variable functions as a mediator if (a) there

is a significant association between the dependent and the independent variable on the

one hand, (b) between the intermediate variable and the dependent variable on the other

hand and (c) if the significant association between the independent and dependent

variable loses its significant when the influence of the intermediate variable is

controlled for (Baron & Kenny, 1986, p. 1176).

The Sobel test (Sobel, 1982, 1986) directly quantifies the relationship between the

independent and dependent variable via the intermediate variable, rather than inferring

its existence from a set of statistical models (Hayes, 2009). This test, also known as the

product of coefficients approach, compares the strength of the indirect effect of the

independent on the dependent variable via an intermediate variable to the null

hypothesis that this effect equals zero. The indirect effect is defined as the product of

path a (the path between the independent variable and intermediate variable) and path b

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(path between the intermediate variable and the dependent variable) (ab). The Sobel

test divides this product by its standard error. This ratio is compared to the standard

normal distribution to test for significance (Preacher & Hayes, 2004, McKinnon, 2007).

The Sobel test was calculated using the spreadsheet by Herr (2006)1.

1Spreadsheet available at http://nrherr.bol.ucla.edu/Mediation/logmed.html

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Results

Descriptives

Table 1 gives an overview of the characteristics of the sample. The sample consists of

1024 participants, with an equal share of men and women (48.2% and 51.8%

respectively). Age ranges from 18 to 92, with a mean of 47.

(Please insert Table 1 here)

Social support as a mediator in the relationship between social capital and health

following the Baron & Kenny procedure (1986)

The Baron & Kenny method is followed for each social capital indicator separately.

Table 2 gives an overview of the statistics resulting from these analyses.

(Please insert Table 2 here)

Having a high number of contacts (NAP) in the low social class (NAP low social class)

is the only social capital indicator that is significantly associated with mental health

(b=0.428, se=0.205, p=0.037). On the other hand, all social capital variables are

significantly associated to social support. There is a significant positive association

between social support and mental health, even after controlling for the different

indicators of social capital.

When the social capital variables and social support are jointly regressed on mental

health, the association between having a high number of contacts in the low social class

(NAP low social class) and mental health is rendered non-significant (b=0.393,

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se=0.207, p=0.058). This implies that social support partially mediates the positive

association between have a high number of contacts in the low social class (NAP low

social class) and mental health. The association between the other indicators of social

capital and mental health remains non-significant after adding social support to the

model (Table 2).

Social support as an intermediate variable in the relationship between social capital

and health: the Sobel test

Table 4 reports the findings of the Sobel test. The Sobel test shows that the indirect

effect of social capital on mental health via social support is significant for almost all of

the social capital indicators. Only for the relationship between the middle level of

number of accessed positions in the low social class (NAP low social class) and mental

health, social support is not identified as a significant intermediate variable (z-value=

1.64).

Resilience as a mediator in the relationship between social capital and health following

the Baron & Kenny procedure (1986)

Having a high level of contacts in the low social class (NAP low social class) is the only

indicator of social capital that is significantly associated with mental health (b=0.428,

se=0.205, p=0.037). Some indicators of social capital are significantly associated to

resilience: the volume of available network resources (middle and high NAP)

(bm=0.409, sem= 0.182, pm=0.024; bh=0.738, seh=0.234, ph=0.002) and having a high

number of contacts in the high social class (High NAP high social class) (b=0.467,

se=0.234, p=0.046) are associated to higher levels of resilience. Resilience and mental

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health are positively and significantly associated, even after controlling for the different

indicators of social capital (p<0.001) (Table 3).

When the social capital variables and resilience are jointly regressed on mental health,

the association between having more contact in the low social class (high NAP low

social class) and mental health is rendered non-significant (b=0.362, se=0.213,

p=0.090). This implies that the positive relation between have a high number of

contacts in the low social class (NAP low social class) and mental health partly runs via

higher levels of resilience. The association between the other indicators of social capital

and mental health remains non-significant after adding resilience to the model (Table 3).

(Please insert Table 3 here)

Resilience as an intermediate variable in the relationship between social capital and

health: the Sobel test

The Sobel test shows that the volume of network resources is the only indicator of

social capital for which the indirect effect of social capital on mental health via

resilience is significant (middle and high NAP) (z-valuem=2.17, z-valueh=2.96) (Table

4).

(Please insert Table 4 here)

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Discussion

Although the relationship between social capital and mental health is well-established,

few researchers have explored the pathways of the mechanisms involved in this

association (Rostila, 2011; Thoits, 2011). Literature research suggests that both social

support and resilience might play a role as intermediate variables in this relationship,

however few studies directly test these pathways. Therefore, this study explores the role

of social support and resilience as intermediate variables in the relationship between

social capital and symptoms of depression and nervousness.

Aspects of social networks have been positively related to resilience, but evidence that

links social capital to resilience is to our knowledge not available. The first hypothesis

of this study therefor is that social capital is positively associated with resilience. The

data support this hypothesis: having access to a higher number of contacts (middle NAP

and high NAP) is related to having a higher level of resilience. This is in accordance

with earlier findings by Wolf and colleagues (2010) who suggest that strong social

networks contribute to a higher level of resilience in elderly during a heat wave.

Furthermore, having more contacts within the high social class (high NAP high social

class) is related to higher levels of resilience.

Secondly, the analyses show that higher levels of resilience are associated with a better

mental health (hypothesis 2). A possible explanation to this finding might be found in

the negative relation between resilience and stress that is reported in literature

(Amirkhan & Greaves, 2003; Dumont & Provost, 1999; Rutter, 1985; Tugade &

Fredrickson, 2004; Lee et al., 2011).

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The main goal of this study was to explore the role of social support and resilience in

the relationship between social capital and mental health. The Baron & Kenny method

suggests that the positive relationship between a high number of contacts in the low

social class and mental health is partially mediated by both social support (hypothesis 3)

and resilience (hypothesis 4). This means that knowing a high number of people in the

low social class is related to a better mental health status, via higher levels of perceived

social support and resilience. This finding can be considered as an addition to earlier

Belgian research that identified social support as an intermediate variable in the

relationship between having more weak ties in the low social class and self-rated health,

although in that case the association between social capital and health was positive

(Verhaeghe et al., 2012). The finding of a positive association between volume of social

resources in the low social class and mental health, is somewhat in contrast to available

literature. People in a higher socio-economic position are believed to possess better

resources, to have access to more valued social resources and to have more knowledge

on how to obtain wanted resources (Lin, 2000). Therefore, the social resource based

approach to social capital states that having access to ties higher on the social ladder

enables better goal attainment in instrumental actions such as job attainment and social

mobility (Lin & Dumin, 1986; Lin, 2001). The present findings suggest that having

access to people in the lower socio-economic strata has an important value, as it seems

that these contacts lead to higher levels of social support.

Additional to the Baron & Kenny method, the Sobel test identified social support as an

intermediate variable in the relationship between having access to more social resources

in general, having a high level of accessed position in the low social class, having a high

27

and middle level of contacts in the high social class and a higher and middle level of

occupational status within one’s network on the one hand and mental health on the other

hand. Resilience was only identified as an intermediate variable in the relationship

between having access to more social resources and mental health.

In sum, the analyses are fairly unanimous on the role of social support as an

intermediate variable in the relationship between social capital and mental health. This

is in accordance to research findings of Verhaeghe et al. (2012) who found that social

support partially mediates the association between diverse indicators of social capital

and self-rated health. The findings regarding the role of resilience are more

contradicting.

Strengths & limitations

The SWING survey has several strengths. Firstly, the survey uses both international

(e.g. Resource Generator, Van der Gaag & Snijders, 2004; MOS Social Support Survey,

Sherbourne & Stewart, 1991) and national (e.g. Social Cohesion in Flanders Survey)

validated instruments. Secondly, an expert panel was consulted during the drafting of

the questionnaire. Additionally, the draft was pretested by means of 11 cognitive

interviews. Thirdly, bias was minimized by redirecting sensitive questions to a self-

administered questionnaire. Moreover, the sample selection and data collection

followed a strict protocol, promoting the representativeness of the sample.

However, some limitations in the study call for caution when interpreting the results.

For instance, the followed operationalisation of social capital represents just one of the

prevailing visions on the concept in literature. Furthermore, the cross-sectional design

28

of this study hinders insight in causal relationships (Kawachi & Berkman, 2001). It is

possible that the relationship between social capital and mental health goes from mental

health to social capital instead of the proposed way, a situation which is also referred to

as ‘reverse causality’ (Thoits, 2011). Literature suggests that the current mental health

state of people influences their social capital (Thoits, 2011), as mental health problems

often have a negative influence on social functioning, resulting in a downward ‘social

drift’ (Goldberg & Morris 1963; Jones et al. 1993; in Cullen & Whiteford, 2001).

Although the method of Baron & Kenny is the most widely used method to assess

mediation, this approach has been criticized (MacKinnon et al., 2007). Baron & Kenny

consider a significant association between the dependent and independent variable to be

a prerequisite for mediation (Preacher & Hayes, 2004). However, this is critiqued in

literature, as the requirement of a significant relationship between X and Y is believed

to reduce the power to detect mediation especially in the case of complete mediation

(MacKinnon et al., 2007). The Sobel-test, based on the product of coefficients, has been

suggested to have higher power to detect indirect effects (MacKinnon et al., 2002).

Nevertheless, the Sobel-test requires a normal approximation of the sample distribution

of the indirect effect (Hayes, 2009), while distributional problems are often encountered

in real research situations (Kenny, 2012; Bollen & Stine, 1990; Stone & Sobel, 1990 in

Hayes, 2009). The use of more advanced techniques such as building structural

equation models, including bootstrapping, to analyse the indirect effect would be a

valuable addition to the current analyses.

29

Recommendations for further research

The current study has explored the role of social support and resilience as intermediate

variables in the relationship between social capital and mental health. However, mental

health is explained by a complex whole of different determinants, such as genetic and

physiological factors, cultural processes and macro-level determinants (Berkman, et al.,

2000, Fisher & Baum, 2010), which could explain the rather limited explained variance

by the analysed models.

Future studies should explore the influence of diverse psychosocial factors.

Furthermore, analyses that include different determinants of mental health (for instance

psychosocial, economic and genetic factors) might be particularly interesting. These

studies enable to compare the impact of diverse health determinants, which might help

both policy makers and researchers to set priorities for further research and investments

(Egan et al., 2008, Almedom, 2005; Fisher & Baum, 2010).

In 2005, De Silva and colleagues concluded their literature review on social capital and

mental health with the statement that the “current evidence is inadequate to inform the

development of specific social capital interventions to combat mental illness.” (De Silva

et al., 2005). The authors claimed that further developments in the research on social

capital were needed, such as analyses on the pathways that link social capital to mental

health and the use of longitudinal studies. This call seems more timely than ever.

The current study finds that having access to more contacts within the low social class is

beneficial for mental health, via higher levels of perceived social support. This seems in

contradiction to the theoretical framework by Nan Lin and colleagues (Lin, 2000, Lin,

2001), which states that having access to occupations higher on the socioeconomic

ladder leads gives access to better social capital, and that better social capital enhances

30

goal attainment (Lin & Dumin, 1986, Lin, 2000, inequality in social capital). However,

this theoretical framework was originally developed in the context of instrumental

actions, actions aimed at gaining new resources (e.g. job attainment). In contrast,

maintaining healthy is seen as an expressive action (i.e. an action with as a goal the

protection of possessed resources) (Lin, 1999). Attention should be paid to expanding

the theoretical framework to specific and substantiated hypotheses on the role of social

resources for expressive actions. Furthermore, literature has suggested the possibility

that the influence of social capital differs for different groups of people (e.g. different

socio-economic groups, men and women,...), and that social capital therefore could

contribute to social inequities (Lin, 2000, inequity in social capital, Song & Lin, 2009).

Further research should try to take this into account.

Conclusion

A vast amount of the Belgian population (14%) suffers from mental health problems

(Gisle, 2008). Social capital is thought to be one of the determinants that contributes to

a better mental health (Conrad, 2010). Although the relationship between social capital

and mental health is already explored, the underlying mechanisms are largely unknown

(Rostila, 2011; Thoits, 2011).

Therefore, this study explored if other psychosocial constructs, i.c. social support and

resilience, influence the relationship between social capital and mental health (research

question).

31

The analyses mainly find evidence for social support as an intermediate variable

between social capital and mental health, but the findings are mixed and differ

according to the used analysis (the Baron & Kenny method (1986) versus the Sobel test

(1982)).

This study contributes to the existing literature, as it tries to unravel the pathways that

underlie the association between social capital and mental health, and includes

resilience in these analyses. In order to inform policy makers and developers of health

interventions on the importance of psychosocial constructs for mental health and the

way in which social capital can contribute to the mental health of individuals, further

research is needed.

33

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43

Tables

Table 1: Descriptives of all variables

N m sd Range

(min-max)

Independent variables

NAP 9.3 4.63 20 (0-20)

low level (reference category) 301

middle level 504

high level 212

NAP in high social class (NAPH) 4.9 2.66 9 (0-9)

low level (reference category) 333

middle level 488

high level 200

NAP in low social class (NAPL) 2.7 1.91 7 (0-7)

low level (reference category) 333

middle level 494

high level 192

Mean ISEI (mISEI) 47.7 9.50 79 (0-79)

low level (reference category) 253

middle level 502

high level 251

Intermediate variables

Social support 42.3 17.61 84 (0-84)

Resilience 7.9 1.58 8 (2-10)

Dependent variable

Mental health (score 100) 74.1 15.23 88 (12-100)

Confounders

Age (years) 47.1 18.34 75 (18-92)

Income per capita (euro) 1582.1 828.33 9868.42 (131.58

-10000)

Note: NAP= number of accessed positions, N=absolute number, m=mean, sd=standarddeviation

44

Table 2: Social support as a mediator in the relationship between social capital and

health following the Baron & Kenny procedure (1986)

Expl.var.(%) b se p

NAP

Model 1 (XY) 5.4

middle level of NAP -0.053 0.17 0.755

high level of NAP 0.252 0.209 0.227

Model 2 (XI) 21.6

middle level of NAP 0.906 0.184 <0.001

high level of NAP 1.470 0.225 <0.001

Model 3 (X,IY) 6.3

middle level of NAP -0.132 0.174 0.447

high level of NAP 0.126 0.214 0.557

social support 0.396 0.149 0.008

NAP high social class

Model 1 (XY) 5.2

middle level of NAPH 0.083 0.163 0.612

high level of NAPH 0.228 0.208 0.273

Model 2 (XI) 24.4

middle level of NAPH 0.990 0.176 <0.001

high level of NAPH 1.827 0.232 <0.001

Model 3 (X,IY) 6.1

middle level of NAPH -0.016 0.167 0.923

high level of NAPH 0.063 0.217 0.773

social support 0.403 0.150 0.007

NAP low social class

Model 1 (XY) 5.6

middle level of NAPL 0.060 0.158 0.704

high level of NAPL 0.428 0.205 0.037

Model 2 (XI) 17.1

middle level of NAPL 0.345 0.165 0.037

high level of NAPL 0.649 0.214 0.002

Model 3 (X,IY) 6.6

middle level of NAPL 0.028 0.159 0.858

high level of NAPL 0.393 0.207 0.058

social support 0.382 0.145 0.009

Mean ISEI

Model 1 (XY) 5.7

middle level of mISEI 0.295 0.172 0.087

45

high level of mISEI 0.277 0.204 0.175

Model 2 (XI) 17.5

middle level of mISEI 0.719 0.183 <0.001

high level of mISEI 0.875 0.217 <0.001

Model 3 (X,IY) 6.6

middle level of mISEI 0.026 0.175 0.196

high level of mISEI 0.185 0.207 0.372

social support 0.386 0.147 0.009

Note: X= mean ISEI, Y= mental health, I= social support, expl.var.= explained variance in Y,

b=regression coefficient, se=standard error, p=significance level

46

Table 3: Resilience as a mediator in the relationship between social capital and

health following the Baron & Kenny procedure (1986)

Expl.var.(%) b se p

NAP

Model 1 (XY) 5.4

middle level of NAP -0.053 0.170 0.755

high level of NAP 0.252 0.209 0.227

Model 2 (XI) 5.0

middle level of NAP 0.409 0.182 0.024

high level of NAP 0.738 0.234 0.002

Model 3 (X,IY) 16.2

middle level of NAP -0.183 0.180 0.309

high level of NAP 0.057 0.218 0.794

resilience 1.442 0.169 <0.001

NAP high social class

Model 1 (XY) 5.2

middle level of NAPH 0.083 0.163 0.612

high level of NAPH 0.228 0.208 0.273

Model 2 (XI) 4.0

middle level of NAPH 0.187 0.174 0.282

high level of NAPH 0.467 0.234 0.046

Model 3 (X,IY) 16.0

middle level of NAPH 0.038 0.171 0.823

high level of NAPH 0.112 0.217 0.605

resilience 1.433 0.168 <0.001

NAP low social class

Model 1 (XY) 5.6

middle level of NAPL 0.060 0.158 0.704

high level of NAPL 0.428 0.205 0.037

Model 2 (XI) 3.8

middle level of NAPL 0.097 0.171 0.571

high level of NAPL 0.374 0.228 0.100

Model 3 (X,IY) 16.4

middle level of NAPL 0.041 0.165 0.803

high level of NAPL 0.362 0.213 0.090

resilience 0.382 0.145 0.009

Mean ISEI

Model 1 (XY) 5.7

middle level of mISEI 0.295 0.172 0.087

47

high level of mISEI 0.277 0.204 0.175

Model 2 (XI) 4.0

middle level of mISEI 0.338 0.180 0.061

high level of mISEI 0.290 0.219 0.186

Model 3 (X,IY) 16.5

middle level of mISEI 0.224 0.181 0.215

high level of mISEI 0.207 0.214 0.333

resilience 1.430 0.170 <0.001

Note: X= mean ISEI, Y= mental health, I=resilience, expl.var.= explained variance in Y,

b=regression coefficient, se=standard error, p=significance level

48

Table 4: Social support as an intermediate variable in the relationship between

social capital and health: the Sobel-test

Middle group High group

Social Support

Number of accessed positions 2.339 2.462

Number of accessed positions in high social class 2.424 2.542

Number of accessed positions in low social class 1.638 1.989

Mean ISEI 2.183 2.200

Resilience

Number of accessed positions 2.173 2.958

Number of accessed positions in high social class 1.066 1.943

Number of accessed positions in low social class 0.566 1.611

Mean ISEI 1.833 1.308

Note: p<0.05(standard normal distribution, df=1)

49

Figures

X Y

I

X Y

Figure 1: The Baron & Kenny steps (1986). Note: X= independent

variable, Y=dependent variable, I= intermediate variable

a b

c

c’